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Mlops Data Engineer Jobs in Spring, TX (NOW HIRING)

Cognizant

Lead AI/ML Developer

Houston, TX · On-site

$133 - $157/hr

Hands-on experience with Azure DevOps, CI/CD pipeline automation, GitHub, Docker, Artifact Registry, and MLOps practices. * Strong understanding of Snowflake, semantic modeling, data quality ...

New

Viridien

Enterprise IT, Data & AI Architect

Houston, TX · On-site

... engineering) and the IT service management. We work together to modernize the digital solutions ... Shape the enterprise AI architecture (platforms, MLOps, governance, Responsible AI). * Identify AI ...

ZipStaff Inc.

Azure Databricks Architect

Houston, TX · On-site

$65 - $67/hr

You will drive data engineering best practices, architect scalable Lakehouse solutions, mentor team ... Exposure to Airflow, Power BI, or DataOps/MLOps practices. * Strong stakeholder management and ...

Hp

AI Data Scientist - Enterprise AI

Spring, TX · On-site

$130K - $205K/yr

... prompt engineering, AI agents. * Familiarity with MLOps, model monitoring, observability, and ... Data Science & Statistical Analysis * Enterprise Problem Solving * Experimentation & Model ...

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All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Spring, TX

Mlops Data Engineer information

See Spring, TX salary details

$39.6K

$115.4K

$158K

How much do mlops data engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for mlops data engineer in Spring, TX is $115,433.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,900.00 and $122,400.00 per year, depending on experience, location, and employer.

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What are the key skills and qualifications needed to thrive as an MLOps data engineer?

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

What are popular job titles related to Mlops Data Engineer jobs in Spring, TX?

For Mlops Data Engineer jobs in Spring, TX, the most frequently searched job titles are:

What job categories do people searching Mlops Data Engineer jobs in Spring, TX look for?

The top searched job categories for Mlops Data Engineer jobs in Spring, TX are:

What cities near Spring, TX are hiring for Mlops Data Engineer jobs?

Cities near Spring, TX with the most Mlops Data Engineer job openings:

Mlops Data Engineer jobs near you
Infographic showing various Mlops Data Engineer job openings in Spring, TX as of June 2026, with employment types broken down into 33% Full Time, 33% Temporary, and 34% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $115,433 per year, or $55.5 per hour.

Lead AI/ML Developer

Cognizant

Houston, TX • On-site

$133 - $157/hr

Other

Medical, Dental, Vision, Life, PTO

Posted yesterday

New

Cognizant rating

7.2

Company rating: 7.2 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

52nd of 72 rated business consultants

Job description

About AI & Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI & Analytics, we work to design the future—a future in which trial-and-error business decisions have been replaced by informed choices and data-supported strategies.

By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence.

Job Summary

We are seeking a Lead AI/ML Developer to design, develop, deploy, and manage enterprise-scale AI/ML, forecasting, predictive analytics, and Generative AI solutions. This role will leverage GCP Vertex AI, Gemini, Claude, Python, PySpark, and modern MLOps frameworks to build scalable, production-ready AI solutions and business-facing analytics products. The ideal candidate will combine hands-on AI/ML engineering expertise with technical leadership experience, cloud-native development, Azure DevOps, and BI integration capabilities. This onsite role in Houston will serve as the lead developer counterpart to the AI/ML engagement lead and support AI/ML delivery within the BI Track Leads group.

*Please note, this role is not able to offer visa transfer or sponsorship now or in the future*

In this role, you will:
  • Lead the end-to-end design, development, deployment, and production support of AI/ML, predictive analytics, forecasting, and Generative AI solutions.
  • Build and operationalize models using GCP Vertex AI, Vertex AI Pipelines, Model Registry, Endpoints, Gemini, Claude, and modern MLOps frameworks.
  • Develop forecasting, classification, regression, recommendation, and Generative AI solutions using Python, PySpark, TensorFlow, PyTorch, and related frameworks.
  • Design and implement RAG-based solutions, prompt engineering patterns, and LLM-powered analytics use cases.
  • Lead the AI/ML development squad, including solution design, code reviews, delivery quality, technical mentoring, and engineering best practices.
  • Own solution design for AI/ML use cases and drive initiatives from prototype through production deployment.
  • Build and manage CI/CD pipelines using Azure DevOps, Artifact Registry, Docker, and automated deployment practices.
  • Integrate ML outputs into BI platforms such as Power BI, Looker, or Tableau to deliver decision-ready dashboards, KPIs, and business insights.
  • Partner with engagement leads, business stakeholders, data engineers, and BI teams on use-case discovery, estimation, roadmap planning, and implementation.
  • Ensure model monitoring, governance, responsible AI compliance, security, data quality, and production reliability across all delivered solutions.
What you need to have to be considered
  • 10+ years of experience in data, analytics, AI/ML, or advanced analytics roles, including 5+ years of hands-on AI/ML development experience.
  • 2+ years of experience leading a developer pod or technical delivery team with responsibility for code quality, mentoring, and solution delivery.
  • Strong hands-on expertise with GCP Vertex AI, Vertex AI Pipelines, Model Registry, model endpoints, and production model deployment.
  • Experience with Gemini, Claude, prompt engineering, RAG architectures, and Generative AI solution development.
  • Strong proficiency in Python, PySpark, SQL, Pandas, Scikit-learn, XGBoost, LightGBM, TensorFlow, and/or PyTorch.
  • Experience building forecasting models such as ARIMA, SARIMA, time series models, predictive analytics models, classification models, and regression models.
  • Hands-on experience with Azure DevOps, CI/CD pipeline automation, GitHub, Docker, Artifact Registry, and MLOps practices.
  • Strong understanding of Snowflake, semantic modeling, data quality, reconciliation, and analytics consumption patterns.
  • Experience with Streamlit development, AngularJS integration, and building business-facing AI/analytics applications is preferred.
  • Experience integrating AI/ML outputs into BI tools such as Power BI, Looker, or Tableau.
  • Strong communication, stakeholder management, technical leadership, and problem-solving skills.
  • Ability to work onsite at the Houston client office and collaborate closely with client and delivery teams.

#LI-EF1

#CB

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Applications will be accepted until 28 Aug 2026.

Salary and Other Compensation:

The annual salary for this position is between $[133,000 - 156,500] depending on experience and other qualifications of the successful candidate.

This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.

Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

  • Medical/Dental/Vision/Life Insurance
  • Paid holidays plus Paid Time Off
  • Long-term/Short-term Disability
  • Paid Parental Leave
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